AEMS: an anytime online search algorithm for approximate policy refinement in large POMDPs

TLDR: A new anytime online search algorithm which seeks to minimize, as efficiently as possible, the error made by an approximate value function computed offline, and shows how previous online computations can be reused in following time steps in order to prevent redundant computations.

Abstract: Solving large Partially Observable Markov Decision Processes (POMDPs) is a complex task which is often intractable. A lot of effort has been made to develop approximate offline algorithms to solve ever larger POMDPs. However, even state-of-the-art approaches fail to solve large POMDPs in reasonable time. Recent developments in online POMDP search suggest that combining offline computations with online computations is often more efficient and can also considerably reduce the error made by approximate policies computed offline. In the same vein, we propose a new anytime online search algorithm which seeks to minimize, as efficiently as possible, the error made by an approximate value function computed offline. In addition, we show how previous online computations can be reused in following time steps in order to prevent redundant computations. Our preliminary results indicate that our approach is able to tackle large state space and observation space efficiently and under real-time constraints.